code: def dummy_image(self):
      batch_size = 1
      num_channels = 3
      sizes = (32, 32)
  
      image = floats_tensor(
          (batch_size, num_channels) + sizes, rng=random.Random(0)
      ).to(torch_device)
      return image
  
imports: import gc
  import random
  import tempfile
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel
  from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, nightly, require_torch_gpu, torch_device
label_desc: Use image node_id 2 for calling the SafeDiffusionPipelineFastTests obj's underlying member method code with example usage: obj.dummy_image() and returns: image